Exploratory analysis and QAQC
Jeremy Wicquart
2020-07-15
Defining functions and packages
# 1. Source functions ----
source("functions/graphical_par.R")
source("functions/theme_graph.R")
source("functions/pred_vbgf.R")
# 2. Required packages ----
library(tidyverse)
library(kableExtra)
library(formattable)
library(readxl)
library(plotly)
library(rfishbase)
# 3. Set theme_graph() as the default ggplot theme ----
theme_set(theme_graph())
# 4. Load data ----
data_complete <- read.csv("./../data/02_back-calculated-size-at-age_morat-et-al.csv")1. Data exploration
1.1 Tables
1.1.1 Data with back-calc.
data_complete %>%
filter(!is.na(Li_sp_m)) %>%
summarize_at(vars("ID", "Species", "Family"), n_distinct, na.rm = TRUE) %>%
bind_rows(data_complete %>%
summarize_at(vars("ID", "Species", "Family"), n_distinct, na.rm = TRUE), .) %>%
mutate(Type = c("All data", "Back-calculated"), .before = 1) %>%
kable(.,
col.names = c("", "Individual", "Species", "Family"),
caption = "Table 1. Comparison of numbers of individuals, species and family for the overall dataset and for back-calculated data") %>%
kable_styling(bootstrap_options = c("striped", "hover"))| Individual | Species | Family | |
|---|---|---|---|
| All data | 855 | 51 | 16 |
| Back-calculated | 710 | 45 | 16 |
1.1.2 Individual by species
data_complete %>%
group_by(Species) %>%
summarise(n = length(unique(ID)),
min_lencap = round(min(Lcpt, na.rm = TRUE), 0),
max_lencap = round(max(Lcpt, na.rm = TRUE), 0),
max_age = max(Agei, na.rm = TRUE)) %>%
kable(.,
col.names = c("Species", "n", "Min length (TL, mm)", "Max length (TL, mm)", "Age max (years)"),
caption = "Table 2. Number of individuals, minimum and maximum total length (mm) and maximum age, by species") %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
column_spec(1, italic = T)| Species | n | Min length (TL, mm) | Max length (TL, mm) | Age max (years) |
|---|---|---|---|---|
| Abudefduf sexfasciatus | 16 | 51 | 172 | 13 |
| Acanthurus achilles | 17 | 130 | 246 | 27 |
| Acanthurus lineatus | 8 | 123 | 362 | 23 |
| Acanthurus nigricans | 8 | 150 | 210 | 9 |
| Acanthurus pyroferus | 18 | 51 | 230 | 19 |
| Acanthurus triostegus | 18 | 147 | 194 | 12 |
| Balistapus undulatus | 22 | 41 | 283 | 18 |
| Caranx melampygus | 7 | 368 | 712 | 15 |
| Centropyge bispinosa | 9 | 28 | 80 | 11 |
| Centropyge flavissima | 28 | 47 | 150 | 27 |
| Cephalopholis argus | 41 | 120 | 450 | 21 |
| Cephalopholis urodeta | 8 | 150 | 214 | 17 |
| Chaetodon citrinellus | 13 | 55 | 101 | 6 |
| Chaetodon ornatissimus | 10 | 122 | 175 | 10 |
| Cheilinus chlorourus | 8 | 96 | 299 | 6 |
| Chlorurus spilurus | 34 | 164 | 344 | 16 |
| Chromis iomelas | 20 | 39 | 61 | 5 |
| Chromis viridis | 6 | 115 | 148 | 9 |
| Ctenochaetus marginatus | 15 | 99 | 270 | 13 |
| Ctenochaetus striatus | 26 | 153 | 218 | 17 |
| Dascyllus aruanus | 20 | 51 | 71 | 7 |
| Dascyllus flavicaudus | 8 | 89 | 104 | 13 |
| Epibulus insidiator | 17 | 157 | 350 | 16 |
| Epinephelus fasciatus | 10 | 123 | 256 | 13 |
| Epinephelus hexagonatus | 16 | 139 | 268 | 14 |
| Epinephelus merra | 46 | 108 | 261 | 17 |
| Epinephelus polyphekadion | 14 | 252 | 514 | 20 |
| Gnathodentex aureolineatus | 8 | 197 | 255 | 17 |
| Gymnosarda unicolor | 4 | 499 | 985 | 7 |
| Halichoeres trimaculatus | 13 | 64 | 186 | 5 |
| Lutjanus fulvus | 12 | 195 | 289 | 21 |
| Lutjanus gibbus | 17 | 113 | 155 | 1 |
| Lutjanus kasmira | 37 | 70 | 326 | 30 |
| Monotaxis grandoculis | 9 | 200 | 505 | 20 |
| Mulloidichthys flavolineatus | 14 | 257 | 311 | 6 |
| Myripristis berndti | 30 | 147 | 279 | 24 |
| Naso lituratus | 17 | 228 | 432 | 10 |
| Naso unicornis | 4 | 488 | 613 | 17 |
| Odonus niger | 18 | 176 | 350 | 16 |
| Ostorhinchus angustatus | 15 | 46 | 83 | 6 |
| Ostorhinchus apogonoides | 30 | 52 | 96 | 7 |
| Parupeneus barberinus | 16 | 82 | 496 | 6 |
| Plectropomus laevis | 31 | 356 | 957 | 22 |
| Pristiapogon taeniopterus | 32 | 51 | 104 | 8 |
| Sargocentron microstoma | 23 | 62 | 204 | 13 |
| Scarus psittacus | 8 | 195 | 311 | 6 |
| Siganus argenteus | 11 | 236 | 388 | 13 |
| Siganus spinus | 4 | 199 | 254 | 3 |
| Stegastes albifasciatus | 7 | 43 | 94 | 8 |
| Stegastes nigricans | 14 | 45 | 138 | 13 |
| Zebrasoma scopas | 18 | 110 | 166 | 24 |
1.1.3 Individuals by location
data_complete %>%
group_by(Species, Location) %>%
summarise(n = length(unique(ID))) %>%
pivot_wider(names_from = Location, values_from = n) %>%
mutate(Gambiers = cell_spec(Gambiers, "html", color = ifelse(is.na(Gambiers), "white", "#446CB3")),
Hao = cell_spec(Hao, "html", color = ifelse(is.na(Hao), "white", "#446CB3")),
Marquesas = cell_spec(Marquesas, "html", color = ifelse(is.na(Marquesas), "white", "#446CB3")),
Moorea = cell_spec(Moorea, "html", color = ifelse(is.na(Moorea), "white", "#446CB3")),
Manuae = cell_spec(Manuae, "html", color = ifelse(is.na(Manuae), "white", "#446CB3")),
Tuamotu = cell_spec(Tuamotu, "html", color = ifelse(is.na(Tuamotu), "white", "#446CB3"))) %>%
kable(.,
format = "html", escape = FALSE,
caption = "Table 3. Number of individuals of each species by location") %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
column_spec(1, italic = T)| Species | Gambiers | Moorea | Manuae | Marquesas | Tuamotu | Hao |
|---|---|---|---|---|---|---|
| Abudefduf sexfasciatus | 11 | 5 | NA | NA | NA | NA |
| Acanthurus achilles | 7 | NA | 10 | NA | NA | NA |
| Acanthurus lineatus | NA | NA | NA | 8 | NA | NA |
| Acanthurus nigricans | NA | NA | NA | 8 | NA | NA |
| Acanthurus pyroferus | NA | NA | NA | 18 | NA | NA |
| Acanthurus triostegus | 3 | 11 | NA | 4 | NA | NA |
| Balistapus undulatus | NA | 14 | NA | 8 | NA | NA |
| Caranx melampygus | NA | 5 | NA | NA | 2 | NA |
| Centropyge bispinosa | NA | 9 | NA | NA | NA | NA |
| Centropyge flavissima | 9 | 7 | NA | 12 | NA | NA |
| Cephalopholis argus | 6 | 10 | NA | NA | 10 | 15 |
| Cephalopholis urodeta | 8 | NA | NA | NA | NA | NA |
| Chaetodon citrinellus | NA | 13 | NA | NA | NA | NA |
| Chaetodon ornatissimus | 4 | NA | NA | 6 | NA | NA |
| Cheilinus chlorourus | 2 | 6 | NA | NA | NA | NA |
| Chlorurus spilurus | 13 | 19 | NA | NA | 2 | NA |
| Chromis iomelas | NA | 20 | NA | NA | NA | NA |
| Chromis viridis | 6 | NA | NA | NA | NA | NA |
| Ctenochaetus marginatus | NA | NA | NA | 15 | NA | NA |
| Ctenochaetus striatus | 11 | 15 | NA | NA | NA | NA |
| Dascyllus aruanus | NA | 20 | NA | NA | NA | NA |
| Dascyllus flavicaudus | 8 | NA | NA | NA | NA | NA |
| Epibulus insidiator | 12 | 5 | NA | NA | NA | NA |
| Epinephelus fasciatus | NA | NA | NA | 10 | NA | NA |
| Epinephelus hexagonatus | NA | NA | NA | NA | NA | 16 |
| Epinephelus merra | 13 | 20 | NA | NA | NA | 13 |
| Epinephelus polyphekadion | NA | NA | NA | NA | NA | 14 |
| Gnathodentex aureolineatus | 8 | NA | NA | NA | NA | NA |
| Gymnosarda unicolor | 4 | NA | NA | NA | NA | NA |
| Halichoeres trimaculatus | 7 | 6 | NA | NA | NA | NA |
| Lutjanus fulvus | NA | 6 | NA | NA | 6 | NA |
| Lutjanus gibbus | NA | NA | NA | 17 | NA | NA |
| Lutjanus kasmira | 2 | NA | NA | 35 | NA | NA |
| Monotaxis grandoculis | 5 | 3 | NA | NA | 1 | NA |
| Mulloidichthys flavolineatus | NA | 14 | NA | NA | NA | NA |
| Myripristis berndti | 7 | 15 | NA | 8 | NA | NA |
| Naso lituratus | 11 | 6 | NA | NA | NA | NA |
| Naso unicornis | 4 | NA | NA | NA | NA | NA |
| Odonus niger | NA | 13 | NA | 5 | NA | NA |
| Ostorhinchus angustatus | NA | 15 | NA | NA | NA | NA |
| Ostorhinchus apogonoides | NA | NA | NA | 30 | NA | NA |
| Parupeneus barberinus | 5 | 4 | NA | 7 | NA | NA |
| Plectropomus laevis | 12 | NA | NA | NA | NA | 19 |
| Pristiapogon taeniopterus | NA | NA | NA | 32 | NA | NA |
| Sargocentron microstoma | NA | 17 | NA | 6 | NA | NA |
| Scarus psittacus | NA | 3 | NA | NA | 5 | NA |
| Siganus argenteus | 6 | 2 | NA | NA | 3 | NA |
| Siganus spinus | NA | 4 | NA | NA | NA | NA |
| Stegastes albifasciatus | NA | 7 | NA | NA | NA | NA |
| Stegastes nigricans | 3 | 11 | NA | NA | NA | NA |
| Zebrasoma scopas | 5 | 13 | NA | NA | NA | NA |
1.1.4 Number of NA
# 1. Number of NA by variable
data_complete %>%
summarise_all(~(sum(is.na(.)))) %>%
t(.) %>%
as.data.frame() %>%
kable(., col.names = c("NA"), caption = "Table 4. Number of rows with Non-Available (NA) data by variable") %>%
kable_styling(bootstrap_options = c("striped", "hover"))| NA | |
|---|---|
| Family | 0 |
| Genus | 0 |
| Species | 0 |
| ID | 0 |
| Agei | 0 |
| Ri | 387 |
| Agecpt | 0 |
| Rcpt | 0 |
| Lcpt | 0 |
| L0p | 0 |
| R0p | 2811 |
| Li_sp_m | 410 |
| Li_sp_sd | 410 |
| Li_sploc_m | 757 |
| Li_sploc_sd | 757 |
| Weight | 603 |
| Location | 0 |
| Observer | 0 |
1.2 Plot
1.2.1 Agei vs Ri
ggplot(data_complete, aes(x = Agei, y = Ri)) +
geom_point(color = col_color_graph, fill = col_fill_graph, size = 1, shape = 21)+
theme(strip.text.x = element_text(size = 8, face = "italic"),
strip.background = element_rect(colour = "black", fill = col_facet, size = 1),
plot.title = element_text(colour = col_color_graph),
plot.subtitle = element_text(colour = "black")) +
facet_wrap(~Species, scales = "free", ncol = 5) +
labs(x = "Age (years)", y = "Radius (mm)")
Figure 1. Relation between otolith radius (Ri) and age (Agei).
1.2.2 Agei vs Li (sp.)
ggplot(data_complete, aes(x = Agei, y = Li_sp_m)) +
geom_point(color = col_color_graph, fill = col_fill_graph, size = 1, shape = 21)+
theme(strip.text.x = element_text(size = 8),
strip.background = element_rect(colour = "black", fill = col_facet, size = 1),
plot.title = element_text(colour = col_color_graph),
plot.subtitle = element_text(colour = "black"),
strip.text.y = element_text(angle = 360, face = "italic")) +
facet_wrap(~Species, scales = "free", ncol = 5) +
labs(x = "Age (years)", y = "Length (mm)")
Figure 2. Relation between length (TL, mm) and age by species.
1.2.3 Agei vs Li (sp. and loc.)
ggplot(data_complete, aes(x = Agei, y = Li_sploc_m)) +
geom_point(color = col_color_graph, fill = col_fill_graph, size = 1, shape = 21)+
theme(strip.text.x = element_text(size = 8),
strip.background = element_rect(colour = "black", fill = col_facet, size = 1),
plot.title = element_text(colour = col_color_graph),
plot.subtitle = element_text(colour = "black"),
strip.text.y = element_text(angle = 360, face = "italic")) +
facet_grid(Species~Location, scales = "free") +
labs(x = "Age (years)", y = "Length (mm)")
Figure 3. Relation between length (TL, mm) and age by species and location.
2. VBGF results
2.1 Growth parameters
# 1. Growth parameters ----
read.csv("./../data/03_back-calculated_vbgf_predictions.csv") %>%
mutate(Estimate = paste0(round(Estimate, 3), " (", round(Est.Error, 3), ")")) %>%
pivot_wider(c("Species", Location), names_from = Parameter, values_from = Estimate) %>%
select(Species, Location, linf, k, t0) %>%
arrange(Species) %>%
kable(.,
col.names = c("Species", "Location", "Linf", "K", "t0"),
caption = "Table 5. Values of Von Bertalanffy parameters estimated through Bayesian framework by species and location. Standard deviation are the values in parentheses. Linf is expressed in TL and cm.") %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
column_spec(1, italic = T)| Species | Location | Linf | K | t0 |
|---|---|---|---|---|
| Abudefduf sexfasciatus | Gambiers | 14.756 (0.442) | 0.933 (0.096) | -0.036 (0.032) |
| Acanthurus achilles | Manuae | 19.203 (0.647) | 0.986 (0.123) | -0.039 (0.036) |
| Acanthurus lineatus | Marquesas | 29.874 (5.013) | 0.326 (0.142) | -0.471 (0.425) |
| Acanthurus nigricans | Marquesas | 16.976 (0.997) | 1.139 (0.177) | -0.02 (0.025) |
| Acanthurus pyroferus | Marquesas | 19.925 (1.267) | 0.472 (0.09) | -0.189 (0.139) |
| Acanthurus triostegus | Moorea | 15.704 (0.509) | 0.628 (0.065) | -0.096 (0.049) |
| Balistapus undulatus | Marquesas | 22.124 (3.352) | 0.31 (0.161) | -0.212 (0.144) |
| Balistapus undulatus | Moorea | 23.579 (7.632) | 0.197 (0.08) | -0.743 (0.571) |
| Caranx melampygus | Moorea | 92.984 (4.784) | 0.085 (0.009) | -0.585 (0.185) |
| Centropyge bispinosa | Moorea | 5.309 (0.889) | 0.897 (0.866) | -0.071 (0.037) |
| Centropyge flavissima | Marquesas | 7.356 (1.75) | 0.905 (0.332) | -0.049 (0.049) |
| Centropyge flavissima | Moorea | 8.197 (0.752) | 1.199 (0.288) | -0.03 (0.027) |
| Centropyge flavissima | Gambiers | 15.774 (3.627) | 0.121 (0.067) | -2.231 (1.364) |
| Cephalopholis argus | Moorea | 36.973 (8.569) | 0.256 (0.109) | -0.313 (0.291) |
| Cephalopholis argus | Tuamotu | 39.694 (7.17) | 0.149 (0.077) | -1.155 (0.719) |
| Cephalopholis argus | Gambiers | 42.924 (1.582) | 0.2 (0.022) | -0.655 (0.174) |
| Cephalopholis argus | Hao | 37.983 (5.701) | 0.25 (0.051) | -0.274 (0.072) |
| Cephalopholis urodeta | Gambiers | 20.459 (1.914) | 0.152 (0.03) | -0.626 (0.169) |
| Chaetodon ornatissimus | Marquesas | 11.167 (6.416) | 1.308 (0.589) | 0.311 (0.584) |
| Chlorurus spilurus | Moorea | 22.599 (1.415) | 1.036 (0.187) | -0.024 (0.042) |
| Chlorurus spilurus | Gambiers | 22.636 (1.175) | 0.701 (0.097) | -0.068 (0.046) |
| Chromis iomelas | Moorea | 5.066 (0.437) | 1.145 (0.242) | -0.06 (0.026) |
| Chromis viridis | Gambiers | 11.812 (1.153) | 0.884 (0.25) | -0.055 (0.06) |
| Ctenochaetus marginatus | Marquesas | 23.135 (2.877) | 0.349 (0.082) | -0.169 (0.077) |
| Ctenochaetus striatus | Moorea | 17.286 (0.565) | 1.013 (0.087) | -0.028 (0.02) |
| Ctenochaetus striatus | Gambiers | 17.078 (0.549) | 0.501 (0.044) | -0.141 (0.056) |
| Dascyllus aruanus | Moorea | 5.607 (0.212) | 0.897 (0.096) | -0.055 (0.028) |
| Dascyllus flavicaudus | Gambiers | 9.325 (0.632) | 0.343 (0.042) | -0.239 (0.076) |
| Epibulus insidiator | Gambiers | 26.751 (1.847) | 0.306 (0.038) | -0.257 (0.078) |
| Epinephelus fasciatus | Marquesas | 22.098 (1.801) | 0.553 (0.077) | -0.05 (0.036) |
| Epinephelus hexagonatus | Hao | 21.735 (1.043) | 0.524 (0.06) | -0.092 (0.052) |
| Epinephelus merra | Moorea | 14.308 (0.907) | 1.126 (0.092) | -0.017 (0.015) |
| Epinephelus merra | Gambiers | 18.697 (0.898) | 0.598 (0.073) | -0.11 (0.059) |
| Epinephelus merra | Hao | 22.536 (0.601) | 0.794 (0.078) | -0.05 (0.037) |
| Epinephelus polyphekadion | Hao | 55.015 (5.758) | 0.153 (0.021) | -0.412 (0.095) |
| Gnathodentex aureolineatus | Gambiers | 20.493 (0.992) | 0.38 (0.056) | -0.288 (0.119) |
| Halichoeres trimaculatus | Gambiers | 18.014 (1.691) | 0.707 (0.148) | -0.034 (0.054) |
| Lutjanus fulvus | Moorea | 19.088 (8.694) | 8.277 (22.6) | -0.073 (0.087) |
| Lutjanus fulvus | Tuamotu | 26.693 (2.498) | 0.295 (0.057) | -0.41 (0.178) |
| Mulloidichthys flavolineatus | Moorea | 25.687 (0.535) | 1.274 (0.091) | -0.013 (0.013) |
| Myripristis berndti | Gambiers | 24.025 (1.876) | 0.272 (0.05) | -0.524 (0.301) |
| Myripristis berndti | Marquesas | 21.911 (0.537) | 0.328 (0.031) | -0.355 (0.111) |
| Myripristis berndti | Moorea | 18.009 (0.964) | 0.434 (0.051) | -0.154 (0.054) |
| Naso lituratus | Gambiers | 31.949 (1.959) | 1.24 (0.176) | -0.01 (0.018) |
| Odonus niger | Moorea | 21.849 (6.476) | 0.621 (0.8) | -0.063 (0.105) |
| Ostorhinchus angustatus | Moorea | 7.405 (0.407) | 0.602 (0.082) | -0.125 (0.041) |
| Ostorhinchus apogonoides | Marquesas | 8.167 (0.211) | 0.918 (0.057) | -0.066 (0.012) |
| Plectropomus laevis | Gambiers | 93.868 (4.547) | 0.143 (0.019) | -0.65 (0.157) |
| Plectropomus laevis | Hao | 91.614 (5.686) | 0.275 (0.031) | -0.026 (0.026) |
| Pristiapogon taeniopterus | Marquesas | 7.992 (0.268) | 0.849 (0.072) | -0.083 (0.024) |
| Sargocentron microstoma | Moorea | 16.284 (0.556) | 0.659 (0.053) | -0.055 (0.028) |
| Siganus argenteus | Gambiers | 34.697 (4.895) | 0.205 (0.06) | -0.439 (0.257) |
| Stegastes nigricans | Moorea | 13.386 (2.113) | 0.312 (0.153) | -0.346 (0.204) |
2.2 Growth curves
# 1. Growth curves ----
read.csv("./../data/03_back-calculated_vbgf_fitted.csv") %>%
ggplot(data = .) +
geom_ribbon(aes(x = age, ymin = ypred_lq, ymax = ypred_uq, fill = Location), alpha = 0.5) +
geom_line(aes(x = age, y = ypred_m, color = Location)) +
geom_point(data = data_complete %>%
mutate(Li_sp_m = Li_sp_m/10),
aes(x = Agei, y = Li_sp_m, color = Location), size = 1) +
facet_wrap(~Species, scales = "free", ncol = 5) +
lims(y = c(0, NA)) +
theme(strip.text.x = element_text(face = "italic")) +
labs(x = "Age (years)", y = "Length (TL, cm)") +
guides(colour = guide_legend(override.aes = list(alpha = 1, size = 0.5)))
Figure 4. Relation between length (TL, mm) and age by species and location.
3. Raw vs Back-calculation
3.1 Growth parameters
# 1. List of species with enough individuals for one location ----
species_list <- c("Abudefduf sexfasciatus", "Acanthurus achilles", "Acanthurus triostegus",
"Chromis iomelas", "Dascyllus aruanus", "Ostorhinchus apogonoides",
"Pristiapogon taeniopterus", "Stegastes nigricans")
# 1. Growth parameters ----
read.csv("./../data/03_raw_vbgf_predictions.csv") %>%
filter(Species %in% species_list) %>%
mutate(Value = paste0(round(Estimate, 3), " (", round(Est.Error, 3), ")")) %>%
select(Species, Location, Parameter, Value) %>%
pivot_wider(1:2, names_from = Parameter, values_from = Value) %>%
select(Species, Location, Linf, K, t0) %>%
arrange(Species) %>%
kable(.,
col.names = c("Species", "Location", "Linf", "K", "t0"),
caption = "Table 5. Values of Von Bertalanffy parameters estimated through Bayesian framework by species and location. Standard deviation are the values in parentheses. Linf is expressed in TL and cm.") %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
column_spec(1, italic = T)| Species | Location | Linf | K | t0 |
|---|---|---|---|---|
| Abudefduf sexfasciatus | Gambiers | 16.187 (0.28) | 1.438 (0.279) | -0.142 (0.241) |
| Acanthurus achilles | Manuae | 21.206 (0.687) | 0.971 (0.273) | -0.062 (0.26) |
| Acanthurus triostegus | Moorea | 17.384 (0.46) | 0.881 (0.242) | -0.054 (0.259) |
| Chromis iomelas | Moorea | 5.419 (0.282) | 1.115 (0.251) | -0.295 (0.153) |
| Dascyllus aruanus | Moorea | 6.241 (0.178) | 0.935 (0.189) | -0.076 (0.253) |
| Ostorhinchus apogonoides | Marquesas | 9.249 (0.284) | 0.559 (0.07) | -0.347 (0.12) |
| Pristiapogon taeniopterus | Marquesas | 9.053 (0.37) | 0.625 (0.136) | -0.261 (0.175) |
| Stegastes nigricans | Moorea | 13.884 (0.763) | 0.34 (0.062) | 0.077 (0.234) |
3.2 Growth curves
# 1. Transform the data to get only Agecpt and Lcpt ----
data_raw <- read.csv("./../data/02_back-calculated-size-at-age_morat-et-al.csv") %>%
filter(Species %in% species_list) %>%
select(Family, Genus, Species, ID, Agecpt, Lcpt, Location, Observer) %>%
unique() %>%
dplyr::group_by(Species, Location) %>%
dplyr::mutate(n = length(unique(ID))) %>%
filter(n >= 10) %>% # filter with at least 10 replicates
ungroup() %>%
dplyr::mutate(Lcpt = Lcpt/10) # Convert to cm
# 2. Make the plot ----
read.csv("./../data/03_raw_vbgf_fitted.csv") %>%
filter(Species %in% species_list) %>%
ggplot(data = .) +
geom_ribbon(aes(x = Agei, ymin = Q2.5, ymax = Q97.5, fill = Location), alpha = 0.5) +
geom_line(aes(x = Agei, y = Estimate, color = Location)) +
geom_point(data = data_raw, aes(x = Agecpt, y = Lcpt, color = Location), size = 1) +
facet_wrap(~Species, scales = "free", ncol = 4) +
lims(y = c(0, NA)) +
theme(strip.text.x = element_text(face = "italic")) +
labs(x = "Age (years)", y = "Length (TL, cm)") +
lims(y = c(0, NA), x = c(0, NA)) +
guides(colour = guide_legend(override.aes = list(alpha = 1, size = 0.5)))
4. Comparison with literature
# 1. Import file synthesizing the VBGF parameters from literature ----
vbgf_literature <- read_excel("./../data/00_von-bertalanffy-literature.xlsx", sheet = 1) %>%
dplyr::mutate(Linf = ifelse(Size_unit == "mm", Linf/10, Linf),
Size_max = ifelse(Size_unit == "mm", Size_max/10, Size_max)) %>% # Convert all length values to cm
select(-Family, -Genus) # Remove Family and Genus to add those level through fishbase
# 2. Check validity of species names ----
vbgf_literature %>%
select(Species) %>%
unique() %>%
left_join(., load_taxa()) %>%
filter(is.na(Genus))## # A tibble: 4 x 8
## Species SpecCode Genus Subfamily Family Order Class SuperClass
## <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Acanthurus chirugus NA <NA> <NA> <NA> <NA> <NA> <NA>
## 2 Chlorurus microrbinos NA <NA> <NA> <NA> <NA> <NA> <NA>
## 3 Scarus psitticus NA <NA> <NA> <NA> <NA> <NA> <NA>
## 4 Cetoscarus bicolour NA <NA> <NA> <NA> <NA> <NA> <NA># 3. Apply the corrections and add Family and Genus ----
vbgf_literature <- vbgf_literature %>%
dplyr::mutate(Species = str_replace_all(Species, c("Acanthurus chirugus" = "Acanthurus chirurgus",
"Chlorurus microrbinos" = "Chlorurus microrhinos",
"Scarus psitticus" = "Scarus psittacus",
"Cetoscarus bicolour" = "Cetoscarus bicolor"))) %>%
left_join(., load_taxa() %>%
select(Species, Genus, Family)) %>%
filter(Species %in% unique(data_complete$Species)) %>%
mutate(Reference = paste0(Reference, " (", Reference_type, ")")) %>%
mutate_at(c("Linf", "K", "t0"), as.character) %>%
select(-Genus, -Family, -Size_unit, -Sex, -Reference_type) %>%
arrange(Species)4.1 Growth parameters
# 1. Comparison of VBGF parameters between our study and literature ----
read.csv("./../data/03_back-calculated_vbgf_predictions.csv") %>%
mutate(Estimate = paste0(round(Estimate, 3), " (", round(Est.Error, 3), ")")) %>%
pivot_wider(c("Species", Location), names_from = Parameter, values_from = Estimate) %>%
select(Species, Location, linf, k, t0) %>%
rename(Linf = linf, K = k) %>%
mutate(Reference = "This study") %>%
full_join(., vbgf_literature) %>%
select(Species, Location, Linf, K, t0, Reference) %>%
arrange(Species) %>%
kable(.,
format = "html", escape = FALSE,
col.names = c("Species", "Location", "Linf", "K", "t0", "Reference"),
caption = "Table 6. Comparison between .") %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
column_spec(1, italic = T)| Species | Location | Linf | K | t0 | Reference |
|---|---|---|---|---|---|
| Abudefduf sexfasciatus | Gambiers | 14.756 (0.442) | 0.933 (0.096) | -0.036 (0.032) | This study |
| Acanthurus achilles | Manuae | 19.203 (0.647) | 0.986 (0.123) | -0.039 (0.036) | This study |
| Acanthurus lineatus | Marquesas | 29.874 (5.013) | 0.326 (0.142) | -0.471 (0.425) | This study |
| Acanthurus lineatus | NA | 18.274 | 0.462 | -0.32 | Choat and Robertson, 2002 (Article) |
| Acanthurus lineatus | Tutuila | 22.1 | 0.12 | -15.6 | Craig et al, 1997 (Article) |
| Acanthurus lineatus | Northern Great Barrier Reef | 18.909 | 0.68 | NA | Gust et al, 2002 (Article) |
| Acanthurus lineatus | Northern Great Barrier Reef | 17.484 | 0.34 | NA | Gust et al, 2002 (Article) |
| Acanthurus lineatus | Pago Pago, American Samoa | 17 | 0.416 | -0.329 | Ralston and Williams, 1988 (Report) |
| Acanthurus nigricans | Marquesas | 16.976 (0.997) | 1.139 (0.177) | -0.02 (0.025) | This study |
| Acanthurus nigricans | NA | 14.286 | 0.28 | -0.69 | Choat and Robertson, 2002 (Article) |
| Acanthurus pyroferus | Marquesas | 19.925 (1.267) | 0.472 (0.09) | -0.189 (0.139) | This study |
| Acanthurus triostegus | Moorea | 15.704 (0.509) | 0.628 (0.065) | -0.096 (0.049) | This study |
| Balistapus undulatus | Marquesas | 22.124 (3.352) | 0.31 (0.161) | -0.212 (0.144) | This study |
| Balistapus undulatus | Moorea | 23.579 (7.632) | 0.197 (0.08) | -0.743 (0.571) | This study |
| Caranx melampygus | Moorea | 92.984 (4.784) | 0.085 (0.009) | -0.585 (0.185) | This study |
| Caranx melampygus | Hawaiian islands | 89.7 | 0.233 | -0.044 | Sudekum et al, 1991 (Article) |
| Centropyge bispinosa | Moorea | 5.309 (0.889) | 0.897 (0.866) | -0.071 (0.037) | This study |
| Centropyge flavissima | Marquesas | 7.356 (1.75) | 0.905 (0.332) | -0.049 (0.049) | This study |
| Centropyge flavissima | Moorea | 8.197 (0.752) | 1.199 (0.288) | -0.03 (0.027) | This study |
| Centropyge flavissima | Gambiers | 15.774 (3.627) | 0.121 (0.067) | -2.231 (1.364) | This study |
| Cephalopholis argus | Moorea | 36.973 (8.569) | 0.256 (0.109) | -0.313 (0.291) | This study |
| Cephalopholis argus | Tuamotu | 39.694 (7.17) | 0.149 (0.077) | -1.155 (0.719) | This study |
| Cephalopholis argus | Gambiers | 42.924 (1.582) | 0.2 (0.022) | -0.655 (0.174) | This study |
| Cephalopholis argus | Hao | 37.983 (5.701) | 0.25 (0.051) | -0.274 (0.072) | This study |
| Cephalopholis argus | Hawaiian islands | 50.6 | 0.075 | -6.5 | Donovan et al, 2013 (Article) |
| Cephalopholis argus | Great Barrier Reef | 48.5499 | 0.049 | -14.145 | Mapleston et al, 2009 (Report) |
| Cephalopholis argus | Egypt, Red Sea | 44.22 | 0.26 | 1.33 | Mehanna et al, 2019 (Article) |
| Cephalopholis argus | Great Barrier Reef | 39.901 | 0.271 | -0.169 | Pears, 2005 (PhD Thesis) |
| Cephalopholis argus | Seychelles | 37.119 | 0.339 | -0.147 | Pears, 2005 (PhD Thesis) |
| Cephalopholis urodeta | Gambiers | 20.459 (1.914) | 0.152 (0.03) | -0.626 (0.169) | This study |
| Cephalopholis urodeta | Cocos Keeling and Christmas islands | 20.645 | 0.31 | NA | Payet et al, 2016 (Article) |
| Cephalopholis urodeta | Papua New Guinea | 14.5 | 1.39 | NA | Fry et al, 2006 (Article) |
| Chaetodon citrinellus | Lizard Island | 9.07 | 2.15 | -0.074 | Berumen et al, 2005 (Article) |
| Chaetodon ornatissimus | Marquesas | 11.167 (6.416) | 1.308 (0.589) | 0.311 (0.584) | This study |
| Chlorurus spilurus | Moorea | 22.599 (1.415) | 1.036 (0.187) | -0.024 (0.042) | This study |
| Chlorurus spilurus | Gambiers | 22.636 (1.175) | 0.701 (0.097) | -0.068 (0.046) | This study |
| Chlorurus spilurus | Hawaiian islands | 34.4 | 0.4 | -0.13 | DeMartini et al, 2017 (Article) |
| Chlorurus spilurus | Guam and Ponhpei, Micronesia | 21.8 | 0.95 | -0.075 | Taylor and Choat, 2014 (Article) |
| Chromis iomelas | Moorea | 5.066 (0.437) | 1.145 (0.242) | -0.06 (0.026) | This study |
| Chromis viridis | Gambiers | 11.812 (1.153) | 0.884 (0.25) | -0.055 (0.06) | This study |
| Ctenochaetus marginatus | Marquesas | 23.135 (2.877) | 0.349 (0.082) | -0.169 (0.077) | This study |
| Ctenochaetus striatus | Moorea | 17.286 (0.565) | 1.013 (0.087) | -0.028 (0.02) | This study |
| Ctenochaetus striatus | Gambiers | 17.078 (0.549) | 0.501 (0.044) | -0.141 (0.056) | This study |
| Ctenochaetus striatus | Tutuila | 17.2 | 0.75 | -0.4 | Trip et al, 2008 (Article) |
| Ctenochaetus striatus | Ta’u | 18.2 | 0.5 | -0.6 | Trip et al, 2008 (Article) |
| Ctenochaetus striatus | Ofu-Olosega | 17.6 | 1.4 | -0.2 | Trip et al, 2008 (Article) |
| Ctenochaetus striatus | Moorea | 17.8 | 0.7 | -0.4 | Trip et al, 2008 (Article) |
| Ctenochaetus striatus | Kavieng | 15.9 | 0.5 | -0.7 | Trip et al, 2008 (Article) |
| Ctenochaetus striatus | Kimbe Bay | 12.8 | 0.6 | -0.7 | Trip et al, 2008 (Article) |
| Ctenochaetus striatus | Lizard island | 19.4 | 1 | -0.3 | Trip et al, 2008 (Article) |
| Ctenochaetus striatus | Lizard island outer reef | 17.1 | 0.9 | -0.4 | Trip et al, 2008 (Article) |
| Ctenochaetus striatus | Townsville | 20.1 | 0.8 | -0.3 | Trip et al, 2008 (Article) |
| Ctenochaetus striatus | One Tree Island | 19.9 | 0.3 | -0.8 | Trip et al, 2008 (Article) |
| Ctenochaetus striatus | Oman | 23.2 | 1.3 | -0.2 | Trip et al, 2008 (Article) |
| Ctenochaetus striatus | Mahe Plateau, Seychelles | 20.5 | 2.1 | -0.1 | Trip et al, 2008 (Article) |
| Ctenochaetus striatus | Amirantes, Seychelles | 18.5 | 1.1 | -0.3 | Trip et al, 2008 (Article) |
| Ctenochaetus striatus | Cocos Keeling lagoon | 18.8 | 0.4 | -0.6 | Trip et al, 2008 (Article) |
| Ctenochaetus striatus | Cocos Keeling | 19.4 | 0.4 | -0.7 | Trip et al, 2008 (Article) |
| Ctenochaetus striatus | NA | 16.8 | 0.591 | -0.27 | Choat and Robertson, 2002 (Article) |
| Ctenochaetus striatus | Bani, Philippines | 13.2 | 0.66 | -0.16 | Fidler et al, 2018 (Article) |
| Ctenochaetus striatus | Bani, Philippines | 11.9 | 0.72 | -0.12 | Fidler et al, 2018 (Article) |
| Ctenochaetus striatus | San Salvador, Philippines | 12.5 | 0.49 | -0.36 | Fidler et al, 2018 (Article) |
| Ctenochaetus striatus | San Salvador, Philippines | 11.9 | 0.72 | -0.12 | Fidler et al, 2018 (Article) |
| Ctenochaetus striatus | Taklobo Farm, Philippines | 12.6 | 0.69 | -0.05 | Fidler et al, 2018 (Article) |
| Ctenochaetus striatus | Taklobo Farm, Philippines | 11.9 | 0.72 | -0.12 | Fidler et al, 2018 (Article) |
| Ctenochaetus striatus | Tutuila Island, American Samoa | 17 | 0.9 | 0.2 | Ochavillo et al, 2011 (Article) |
| Ctenochaetus striatus | Pago Pago, American Samoa | 25.6 | 0.424 | -0.643 | Ralston and Williams, 1988 (Report) |
| Dascyllus aruanus | Moorea | 5.607 (0.212) | 0.897 (0.096) | -0.055 (0.028) | This study |
| Dascyllus flavicaudus | Gambiers | 9.325 (0.632) | 0.343 (0.042) | -0.239 (0.076) | This study |
| Epibulus insidiator | Gambiers | 26.751 (1.847) | 0.306 (0.038) | -0.257 (0.078) | This study |
| Epibulus insidiator | Palm Islands, Great Barrier Reef | 19.894 | 0.25 | -0.172 | Hubble, 2003 (PhD Thesis) |
| Epinephelus fasciatus | Marquesas | 22.098 (1.801) | 0.553 (0.077) | -0.05 (0.036) | This study |
| Epinephelus fasciatus | Great Barrier Reef | 28.1814 | 0.405 | -0.685 | Mapleston et al, 2009 (Report) |
| Epinephelus fasciatus | Great Barrier Reef | 28.213 | 0.469 | -0.198 | Pears, 2005 (PhD Thesis) |
| Epinephelus fasciatus | Seychelles | 27.46 | 0.45 | -0.212 | Pears, 2005 (PhD Thesis) |
| Epinephelus hexagonatus | Hao | 21.735 (1.043) | 0.524 (0.06) | -0.092 (0.052) | This study |
| Epinephelus merra | Moorea | 14.308 (0.907) | 1.126 (0.092) | -0.017 (0.015) | This study |
| Epinephelus merra | Gambiers | 18.697 (0.898) | 0.598 (0.073) | -0.11 (0.059) | This study |
| Epinephelus merra | Hao | 22.536 (0.601) | 0.794 (0.078) | -0.05 (0.037) | This study |
| Epinephelus merra | Reunion island | 21.2 | 0.5 | -0.009 | Pothin et al, 2004 (Article) |
| Epinephelus polyphekadion | Hao | 55.015 (5.758) | 0.153 (0.021) | -0.412 (0.095) | This study |
| Epinephelus polyphekadion | Pohnpei | 44.71 | 0.251 | -0.14 | Rhodes et al, 2011 (Article) |
| Epinephelus polyphekadion | Aldabra island | 57.9 | 0.18 | 0 | Grandcourt, 2005 (Article) |
| Epinephelus polyphekadion | Yaeyama islands | 56.58 | 0.139 | -1.18 | Ohta et al, 2017 (Article) |
| Epinephelus polyphekadion | Great Barrier Reef | 56.2345 | 0.194 | -0.081 | Mapleston et al, 2009 (Report) |
| Epinephelus polyphekadion | Great Barrier Reef | 53.701 | 0.195 | -0.245 | Pears, 2005 (PhD Thesis) |
| Epinephelus polyphekadion | Seychelles | 58.237 | 0.206 | -0.212 | Pears, 2005 (PhD Thesis) |
| Gnathodentex aureolineatus | Gambiers | 20.493 (0.992) | 0.38 (0.056) | -0.288 (0.119) | This study |
| Halichoeres trimaculatus | Gambiers | 18.014 (1.691) | 0.707 (0.148) | -0.034 (0.054) | This study |
| Lutjanus fulvus | Moorea | 19.088 (8.694) | 8.277 (22.6) | -0.073 (0.087) | This study |
| Lutjanus fulvus | Tuamotu | 26.693 (2.498) | 0.295 (0.057) | -0.41 (0.178) | This study |
| Lutjanus fulvus | Yaeyama islands | 26.5 | 0.41 | -0.49 | Shimose and Nanami, 2014 (Article) |
| Lutjanus gibbus | New Caledonia | 35.13 | 0.29 | -1 | Moore, 2019 (Article) |
| Lutjanus gibbus | Ishigaki islands | 39.05 | 0.21 | -1.88 | Nanami et al, 2010 (Article) |
| Lutjanus gibbus | Ishigaki islands | 30.34 | 0.256 | -3.05 | Nanami et al, 2010 (Article) |
| Lutjanus gibbus | Great Barrier Reef | 54.4 | 0.06 | -9.48 | Heupel et al, 2010b (Article) |
| Lutjanus gibbus | Great Barrier Reef | 35.2 | 0.51 | NA | Currey et al, 2010 (Report) |
| Lutjanus gibbus | Indonesia | 27.4 | 0.78 | -0.24 | Holloway et al, 2015 (Article) |
| Lutjanus kasmira | Hawaiian islands | 34 | 0.29 | -1.37 | Morales-Nin and Ralston, 1990 (Article) |
| Lutjanus kasmira | Pago Pago, American Samoa | 29.6 | 0.384 | -1.349 | Ralston and Williams, 1988 (Report) |
| Mulloidichthys flavolineatus | Moorea | 25.687 (0.535) | 1.274 (0.091) | -0.013 (0.013) | This study |
| Mulloidichthys flavolineatus | Hurghada | 38 | 0.27 | -1.15 | Mehanna et al, 2017 (Article) |
| Myripristis berndti | Gambiers | 24.025 (1.876) | 0.272 (0.05) | -0.524 (0.301) | This study |
| Myripristis berndti | Marquesas | 21.911 (0.537) | 0.328 (0.031) | -0.355 (0.111) | This study |
| Myripristis berndti | Moorea | 18.009 (0.964) | 0.434 (0.051) | -0.154 (0.054) | This study |
| Naso lituratus | Gambiers | 31.949 (1.959) | 1.24 (0.176) | -0.01 (0.018) | This study |
| Naso lituratus | Pohnpei | 21.2 | 1.38 | -0.2 | Taylor et al, 2014 (Article) |
| Naso lituratus | Guam | 20.4 | 0.93 | -0.3 | Taylor et al, 2014 (Article) |
| Naso unicornis | Hawaiian islands | 47.8 | 0.44 | -0.12 | Andrews et al, 2016 (Article) |
| Naso unicornis | Pohnpei | 47.1 | 0.36 | -0.14 | Taylor et al, 2014 (Article) |
| Naso unicornis | Guam | 49.3 | 0.22 | -0.48 | Taylor et al, 2014 (Article) |
| Naso unicornis | NA | 38.577 | 0.489 | -0.14 | Choat and Robertson, 2002 (Article) |
| Odonus niger | Moorea | 21.849 (6.476) | 0.621 (0.8) | -0.063 (0.105) | This study |
| Ostorhinchus angustatus | Moorea | 7.405 (0.407) | 0.602 (0.082) | -0.125 (0.041) | This study |
| Ostorhinchus apogonoides | Marquesas | 8.167 (0.211) | 0.918 (0.057) | -0.066 (0.012) | This study |
| Plectropomus laevis | Gambiers | 93.868 (4.547) | 0.143 (0.019) | -0.65 (0.157) | This study |
| Plectropomus laevis | Hao | 91.614 (5.686) | 0.275 (0.031) | -0.026 (0.026) | This study |
| Plectropomus laevis | Aldabra island | 101.5 | 0.19 | 0 | Grandcourt, 2005 (Article) |
| Plectropomus laevis | Great Barrier Reef | 115.9 | 0.096 | -2.28 | Heupel et al, 2010a (Article) |
| Pristiapogon taeniopterus | Marquesas | 7.992 (0.268) | 0.849 (0.072) | -0.083 (0.024) | This study |
| Sargocentron microstoma | Moorea | 16.284 (0.556) | 0.659 (0.053) | -0.055 (0.028) | This study |
| Scarus psittacus | NA | 17.21 | 1.19 | -0.05 | Choat and Robertson, 2002 (Article) |
| Scarus psittacus | Hawaiian islands | 32.7 | 0.486 | -0.01 | DeMartini et al, 2017 (Article) |
| Scarus psittacus | Tutuila Island, American Samoa | 21.7 | 1.653 | -0.29 | Page, 1998 (Report) |
| Scarus psittacus | Guam and Ponhpei, Micronesia | 20.7 | 0.91 | -0.083 | Taylor and Choat, 2014 (Article) |
| Siganus argenteus | Gambiers | 34.697 (4.895) | 0.205 (0.06) | -0.439 (0.257) | This study |
| Siganus argenteus | Mariana islands | 27.4 | 0.9 | -0.3 | Taylor et al, 2016 (Article) |
| Stegastes nigricans | Moorea | 13.386 (2.113) | 0.312 (0.153) | -0.346 (0.204) | This study |
| Zebrasoma scopas | NA | 13.282 | 0.425 | -0.49 | Choat and Robertson, 2002 (Article) |
4.2 Growth curves
# 1. Get predictions from literature data ----
vbgf_literature_pred <- vbgf_literature %>%
# Complete missing Age_max
filter(is.na(Age_max)) %>%
left_join(., vbgf_literature %>%
group_by(Species) %>%
summarise(Age_max2 = max(Age_max, na.rm = TRUE)) %>%
mutate(Age_max2 = na_if(Age_max2, -Inf))) %>%
mutate(Age_max = ifelse(is.na(Age_max), Age_max2, Age_max)) %>%
select(-Age_max2) %>%
bind_rows(., vbgf_literature %>%
filter(!(is.na(Age_max)))) %>%
arrange(Species) %>%
# Fix missing t0 to 0
dplyr::mutate(t0 = ifelse(is.na(t0), 0, t0)) %>%
filter(!(is.na(K)), !(is.na(Linf)), !(is.na(t0))) %>%
mutate_at(c("Linf", "K", "t0"), as.numeric) %>%
filter(Species %in% unique(read.csv("./../data/03_back-calculated_vbgf_fitted.csv")$Species)) %>%
# Make the predictions
dplyr::mutate(Line = row_number()) %>%
group_by(Line) %>%
do(pred_vbgf(data = .))
# 2. Make the plot ----
plot_final <- read.csv("./../data/03_back-calculated_vbgf_fitted.csv") %>%
filter(Species %in% unique(vbgf_literature_pred$Species)) %>%
ggplot(data = .) +
geom_ribbon(aes(x = age, ymin = ypred_lq, ymax = ypred_uq, group = Location, fill = Location), alpha = 0.5) +
geom_line(aes(x = age, y = ypred_m, color = Location), size = 0.7) +
geom_line(data = vbgf_literature_pred, aes(x = Agei, y = Li, group = as.factor(Line), text = Reference), size = 0.7) +
facet_wrap(~Species, ncol = 4, scales = "free") +
labs(x = "Age (years)", y = "Length (cm)") +
theme(strip.text.x = element_text(face = "italic")) +
lims(y = c(0, NA), x = c(0, NA)) +
guides(colour = guide_legend(override.aes = list(alpha = 1, size = 0.5)))
plot_final
4.3 Maximum age
# 1. Comparison of the maximum age between our study and literature ----
vbgf_literature %>%
filter(!(is.na(Age_max))) %>%
group_by(Species) %>%
summarise(Age_max = max(Age_max)) %>%
ungroup() %>%
left_join(., vbgf_literature) %>%
select(Species, Age_max, Reference) %>%
right_join(., data_complete %>%
mutate(Lcpt = Lcpt/10) %>%
filter(!(is.na(Agecpt))) %>%
group_by(Species) %>%
summarise(Agecpt = max(Agecpt)) %>%
ungroup()) %>%
select(Species, Agecpt, Age_max, Reference) %>%
arrange(Species) %>%
mutate(Agecpt = as.numeric(as.character(Agecpt)),
Age_max = as.numeric(as.character(Age_max))) %>%
mutate(Species = cell_spec(Species, "html", color = "black"),
Agecpt = cell_spec(Agecpt, "html", color = "black"),
Age_max = cell_spec(Age_max, "html", color = ifelse(is.na(Age_max), "white", "black")),
Reference = cell_spec(Reference, "html", color = ifelse(is.na(Reference), "white", "black"))) %>%
kable(.,
format = "html", escape = FALSE,
col.names = c("Species", "Age max.", "Age max.", "Reference"),
caption = "Table 4. Comparison of maximum age (Age max.) between this study and litterature. When value of Age max. from literature is missing for one species, it can be either due to unspecified maximum age in the reference or absent value in literature for the considered species.") %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
column_spec(1, italic = T) %>%
add_header_above(c(" " = 1, "This Study" = 1, "Litterature" = 2))| Species | Age max. | Age max. | Reference |
|---|---|---|---|
| Abudefduf sexfasciatus | 13 | NA | NA |
| Acanthurus achilles | 27 | NA | NA |
| Acanthurus lineatus | 23 | 42 | Gust et al, 2002 (Article) |
| Acanthurus nigricans | 9 | NA | NA |
| Acanthurus pyroferus | 19 | NA | NA |
| Acanthurus triostegus | 12 | NA | NA |
| Balistapus undulatus | 18 | NA | NA |
| Caranx melampygus | 15 | 6 | Sudekum et al, 1991 (Article) |
| Centropyge bispinosa | 11 | NA | NA |
| Centropyge flavissima | 27 | NA | NA |
| Cephalopholis argus | 21 | 39 | Mapleston et al, 2009 (Report) |
| Cephalopholis urodeta | 17 | 12 | Payet et al, 2016 (Article) |
| Chaetodon citrinellus | 6 | 7 | Berumen et al, 2005 (Article) |
| Chaetodon ornatissimus | 10 | NA | NA |
| Cheilinus chlorourus | 6 | NA | NA |
| Chlorurus spilurus | 16 | 11 | DeMartini et al, 2017 (Article) |
| Chromis iomelas | 5 | NA | NA |
| Chromis viridis | 9 | NA | NA |
| Ctenochaetus marginatus | 13 | NA | NA |
| Ctenochaetus striatus | 17 | 37 | Trip et al, 2008 (Article) |
| Dascyllus aruanus | 7 | NA | NA |
| Dascyllus flavicaudus | 13 | NA | NA |
| Epibulus insidiator | 16 | 16 | Hubble, 2003 (PhD Thesis) |
| Epinephelus fasciatus | 13 | 21 | Mapleston et al, 2009 (Report) |
| Epinephelus hexagonatus | 14 | NA | NA |
| Epinephelus merra | 17 | 6 | Pothin et al, 2004 (Article) |
| Epinephelus polyphekadion | 20 | 44 | Mapleston et al, 2009 (Report) |
| Gnathodentex aureolineatus | 17 | NA | NA |
| Gymnosarda unicolor | 7 | NA | NA |
| Halichoeres trimaculatus | 5 | NA | NA |
| Lutjanus fulvus | 21 | 34 | Shimose and Nanami, 2014 (Article) |
| Lutjanus gibbus | 1 | 38 | Moore, 2019 (Article) |
| Lutjanus kasmira | 30 | NA | NA |
| Monotaxis grandoculis | 20 | NA | NA |
| Mulloidichthys flavolineatus | 6 | 6 | Mehanna et al, 2017 (Article) |
| Myripristis berndti | 24 | NA | NA |
| Naso lituratus | 10 | 14 | Taylor et al, 2014 (Article) |
| Naso unicornis | 17 | 50 | Andrews et al, 2016 (Article) |
| Odonus niger | 16 | NA | NA |
| Ostorhinchus angustatus | 6 | NA | NA |
| Ostorhinchus apogonoides | 7 | NA | NA |
| Parupeneus barberinus | 6 | NA | NA |
| Plectropomus laevis | 22 | 16 | Heupel et al, 2010a (Article) |
| Pristiapogon taeniopterus | 8 | NA | NA |
| Sargocentron microstoma | 13 | NA | NA |
| Scarus psittacus | 6 | 6 | DeMartini et al, 2017 (Article) |
| Scarus psittacus | 6 | 6 | Taylor and Choat, 2014 (Article) |
| Siganus argenteus | 13 | NA | NA |
| Siganus spinus | 3 | NA | NA |
| Stegastes albifasciatus | 8 | NA | NA |
| Stegastes nigricans | 13 | NA | NA |
| Zebrasoma scopas | 24 | NA | NA |
5. Annex
# 1. Variables description ----
data.frame(Column = 1:ncol(data_complete),
Variable = colnames(data_complete),
Description = NA,
Unit = NA,
Type = str_to_title(as.vector(sapply(data_complete, class)))) %>%
left_join(., data_complete %>%
mutate_if(is.character, ~NA) %>%
summarise_all(., ~paste0(round(min(., na.rm = TRUE), 3), " - ", round(max(., na.rm = TRUE), 3))) %>%
pivot_longer(everything(), names_to = "Variable", values_to = "Range") %>%
mutate(Range = na_if(Range, "Inf - -Inf"))) %>%
write.csv2(., "./../doc/03_resubmission_scientific-data/variables_description.csv", row.names = FALSE)# 1. Extract the characteristics of individual used as otoliths section examples ----
sections_example <- data_complete %>%
filter(ID %in% c("GAM18_B006", "AC_LI_MA_03_17_221", "AC_NI_MA_03_17_118", "AC_PY_MA_03_17_009", "MOO18FE167",
"GAM18_B112", "CT_MA_MA_03_17_396", "GAM18_A033", "GAM18_A059", "GAM18_B080", # Acanthuridae
"OS_AP_MA_03_17_284", "PR_TA_MA_03_17_101", # Apogonidae
"OD_NI_MA_03_17_045", "BA_UN_MO_03_16_003", # Balistidae
"GAM18_A100", # Chaetodontidae
"GAM18_A156", "SA_MI_MA_03_17_128", # Holocentridae
"GAM18_B066", "GAM18_A011", "GAM18_A214", # Labridae
"GAM18_B105", "GAM18_B093", # Lethrinidae
"LU_GI_MA_03_17_058", "GAM18_B099", # Lutjanidae
"GAM18_B129", # Mullidae
"GAM18_A135", "CE_BI_MO_03_16_002", "GAM18_B056", "GAM18_A162", "DA_AR_MO_03_16_011",
"GAM18_A036", "GAM18_A191", "CH_JO_MO_03_16_013", # Pomacentridae
"GAM18_A120", "SC_PS_TA_03_16_019", # Scaridae
"GAM18_B012", # Scombridae
"GAM18_A073", "GAM18_A089", "EP_FA_MA_03_17_027", "H33", "GAM18_A154", "H70", "GAM18_B052", # Serranidae
"GAM18_B019", "SI_SP_MO_03_16_008")) %>% # Siganidae
filter(Agei == 0) %>%
select(Family, Species, ID, Agecpt, Lcpt, Location) %>%
arrange(Family, Species) %>%
mutate(Legend = paste0(row_number(), ". ", Species, " (", Agecpt, " years, ", Lcpt, " mm TL)"))
# 2. Export the txt file needed for the legend of the figure on otoliths sections ----
write.table(paste(sections_example$Legend, collapse = ", "),
"./../doc/03_resubmission_scientific-data/legend_fig_otolith_sections.txt",
col.names = FALSE, row.names = FALSE)Reproducibility
## R version 4.0.1 (2020-06-06)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18363)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=French_France.1252 LC_CTYPE=French_France.1252
## [3] LC_MONETARY=French_France.1252 LC_NUMERIC=C
## [5] LC_TIME=French_France.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] rfishbase_3.0.4 plotly_4.9.2.1 readxl_1.3.1
## [4] formattable_0.2.0.1 kableExtra_1.1.0 forcats_0.5.0
## [7] stringr_1.4.0 dplyr_1.0.0 purrr_0.3.4
## [10] readr_1.3.1 tidyr_1.1.0 tibble_3.0.1
## [13] ggplot2_3.3.2 tidyverse_1.3.0 RColorBrewer_1.1-2
## [16] extrafont_0.17
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.1 jsonlite_1.6.1 viridisLite_0.3.0 modelr_0.1.8
## [5] assertthat_0.2.1 highr_0.8 blob_1.2.1 cellranger_1.1.0
## [9] yaml_2.2.1 Rttf2pt1_1.3.8 pillar_1.4.4 backports_1.1.7
## [13] lattice_0.20-41 glue_1.4.1 extrafontdb_1.0 digest_0.6.25
## [17] rvest_0.3.5 colorspace_1.4-1 htmltools_0.5.0 pkgconfig_2.0.3
## [21] broom_0.5.6 haven_2.3.1 scales_1.1.1 webshot_0.5.2
## [25] generics_0.0.2 farver_2.0.3 ellipsis_0.3.1 withr_2.2.0
## [29] lazyeval_0.2.2 cli_2.0.2 magrittr_1.5 crayon_1.3.4
## [33] memoise_1.1.0 evaluate_0.14 fs_1.4.1 fansi_0.4.1
## [37] nlme_3.1-148 xml2_1.3.2 tools_4.0.1 gh_1.1.0
## [41] data.table_1.12.8 hms_0.5.3 lifecycle_0.2.0 munsell_0.5.0
## [45] reprex_0.3.0 compiler_4.0.1 rlang_0.4.6 grid_4.0.1
## [49] rstudioapi_0.11 htmlwidgets_1.5.1 crosstalk_1.1.0.1 labeling_0.3
## [53] rmarkdown_2.3 gtable_0.3.0 DBI_1.1.0 curl_4.3
## [57] R6_2.4.1 lubridate_1.7.9 knitr_1.28 utf8_1.1.4
## [61] stringi_1.4.6 Rcpp_1.0.4.6 vctrs_0.3.1 dbplyr_1.4.4
## [65] tidyselect_1.1.0 xfun_0.14Jeremy WICQUART | jeremywicquart@gmail.com | 2020-07-15 09:30:21